so ive been working on the back end of a OS tailored for ai development and cognitive learning
i have the service section down - meaning i can run the various pipelines issues are presently the ethics engine causes issues
i have a number of sub pipelines in the ecosystem but the one im concerned with
Curious if anyone's dealt with dynamic import failures across a distributed agent chain where each agent uses isolated OpenAI key rotations?
I’ve got a reinforcement-driven AI pipeline (FAISS + custom Nexus index sharding) with 12 modular agents (agent_00_generator → agent_11_verifier) loaded dynamically via a hub-level CDI (Citadel Dynamic Importer).
After the pipeline loads, each agent injects enriched embeddings into a shared vector storage layer (with metadata for scoring, novelty, tiering, etc). But once I added key-based self-healing (get_random_api_key w/ per-agent fallback), I started getting silent dynamic import failures, mostly ImportError or ModuleNotFoundError traced back to index managers or key loaders.
Has anyone built agent-based pipelines using import governance frameworks + rotating LLM API keys?
Open to comparing boot logic, failover policies, or index consistency guarantees (I’m using tiered STM/LTM vector tagging, rejection shunts, and debate-based enrichment in Senate). Not afraid of async monkeypatching if that's the route—just trying to avoid boilerplate catch/retry blocks in every module.
the system generates monolithic kernels full functional here is a older one attatched. The code " thought debate council game" IS a ochestrator - user friendly u COULD take it and change it, all i ask is if u do - give credit where credit is due. I had no plans on changing that file it was just a proof of concept that the system could understand and design and deliver entire functional kernels or ochestrators of agents.